As wireless services and associated mobile devices continue to become a commodity, competitive pressure on wireless network service providers with respect to service quality or operating costs continues to grow. Indoor coverage is among the primary differentiator of wireless service quality. For wireless network based on outdoor deployment of base stations, building new cell sites is one approach to improve indoor coverage, and reduce customer attrition and associated subscriber re-acquisition costs. Yet, excessive cell site building can lead to higher cost of service provision, reduced margins and subscribed churn due, for example, to pricing pressures. An approach to balance cost(s) and benefit(s) of cell deployment growth consists of acquisition of information on a service provider network and those of competing providers to which a subscriber may migrate.
Conventional techniques for residential indoor service quality measurements are typically impractical since such techniques require expensive test equipment, labor and permission to enter the residence. The alternative, however, generally includes operation of expensive vehicles fitted with complex and expensive test equipment and operated by highly trained, costly radio-frequency (RF) engineers; the vehicle driven through sample neighborhoods acquiring measurements from vehicle-mounted antennas. Detail and accuracy of such indirect measurements often are sacrificed in exchange for cost and time containment. More importantly, even the best conventional measurements excluded indoor areas. Consequently, conventional approaches to probing indoor quality of service generally lead to aggressive data processing and application of ad-hoc “correction factors” to extrapolate indoor coverage statistics from outdoor measurements. While modeling and simulation of wireless signal propagation can be sophisticated on a per-residence level, different residence layout, building materials and other realistic indoor coverage factors, such as RF radiation scattering, render even the most complex model impractical for application over a significant, statistically significant sample of neighborhoods and residences. Accordingly, information extracted from conventional approaches to assessment of indoor wireless service quality generally results in unsatisfactory actionable information and ensuing faulty decision-making and strategic planning in connection with cell site growth, service quality and forecasted operational margins.